Information described in this section does not constitute the prior art, but simply provides background information on the present embodiment.
In a wireless technique, a network connection point is separated unlike in a wired technique, and thus a broadcasting communication is basically performed between a device and a wireless connection device. Thus, a hacker may make a variety of attacks such as DOS (disk operating system), phishing, man-in-the-middle attacks, etc., by utilizing information obtained by monitoring a traffic in which individual devices communicate with one another even without making a special effort. Of course, a network administrator may also monitor such a traffic situation as does the hacker and detect the occurrence of such attacks and threats, and thereby respond to the attacks and threats.
However, there is a problem in that clone AP data of a hacker who identically replicates device identification information such as applied AP′ (access point) MAC (media access control) address, SSID (service set identification), BSSID (basic service set identification), etc. and data of the applied AP are transmitted and received mixed, and therefore in this case, it is very important to detect whether wireless attacks occur, distinguish which one is a data frame sent by the hacker, and identify the location of a device corresponding to the hacker by considering the intensity of the received signals.
Therefore, in a fingerprinting technique, it is important to extract characteristics by which a specific device can be uniquely identified and classified, that is, a fingerprint through an analysis of information (e.g., physical hardware layer information such as in a wireless modem, MAC software layer information such as in a beacon header, etc.) received from a specific radio device, and there are a variety of techniques depending on the method of extracting these characteristics.
A fingerprinting mechanism is largely divided into fingerprint generating and classifying steps, and the fingerprint generating step is a step of collecting and processing a wireless signal transmitted by a device and extracting features by which a device can be uniquely identified, and the fingerprint classifying step is a step of classifying the feature values extracted in the generating step in a statistical manner and determining whether the classified feature values match with a fingerprint of the corresponding device extracted and stored during a learning process, thereby determining whether the corresponding device is a clone device.
Based on the determination result, when it is a normal device rather than a clone device, the corresponding device is recognized as a device of an authorized user, but in the prior art, when feature values of a wireless environment extracted in the generating step and the fingerprint of the device coincided with each other in a primary comparison, the corresponding device was recognized as a normal device based on the comparison result.
However, this approach has had a problem in that a clone device having the same fingerprint as that of a normal device may be manufactured by replicating the feature values of the wireless environment extracted in the generating step so that the clone device may again be recognized as a normal device. Therefore, on the one hand, a user authentication method with a high level of security is needed, and, on the other hand, a method is needed that can be utilized in fields other than simple user authentication using a high level of security and characteristics of an RF fingerprint.
Meanwhile, with the development of the mobile communications network and terminal specifications, mobile communication terminals have become essential belongings of modern people that penetrate boundaries of conventional simple communications devices or information providing devices and are showing a trend of evolving into total entertainment devices.
In addition, nowadays, use of a smart phone in which functions of a communications terminal and a PDA (personal digital assistant) are combined is popular. In such a smart phone, due to being an intelligent terminal to which computer support functions such as Internet communication, information search, etc. are added, a larger-capacity memory and a higher-performance CPU (central processing unit) are mounted compared to an existing communications terminal, and an OS (operating system) for supporting the execution of a variety of applications, voice/data communication, PC (personal computer) linkage, and the like is mounted.
As one of the application technologies using such a smart phone, a variety of location-based services (e.g., vehicle navigation device, map, path finding, indoor store guidance, etc.) that provide convenience to users have been introduced and received a lot of attention.
In general, the location-based services used outdoors uses a location tracking technique employing a GPS (global positioning system), and the location-based services used indoors mainly uses a network-based positioning technique such as RFID, Bluetooth, Wi-Fi, etc.
As such indoor positioning techniques, there are techniques including Cell-ID, triangulation, fingerprint, and the like.
The Cell-ID technique estimates a current location based on an AP (access point) which is closest to a location measurement object in the vicinity. While this technique is simple to implement, positioning accuracy is not high. Also while the triangulation technique basically provides a high location resolution, a problem is that location error may be increased by a phenomenon such as multipath fading. Further, since a special device capable of accurately obtaining this information is required, construction cost is increased. Thus, the fingerprint technique is preferred over the above-mentioned two techniques. The fingerprint technique arbitrarily selects a plurality of locations in a service area in advance and estimates a location using RSS (received signal strength) collected at the selected location.
In addition, in recent years, as the spread of a wireless communications network using Wi-Fi expands, millions of APs are installed everywhere in a city, and signal transmissions from beacon devices are also increasing due to provisions of a variety of beacon services. As a result, most stores may receive wireless signals of the AP or the beacon devices, and with an increase in the density of the installed APs, a mobile communications terminal device located indoors may estimate the location. On the other hand, in the case of a provider of such an AP or a beacon device, the provider may be aware of information on which store has a device installed by the provider itself, and based on this, may easily provide location-based services or service contents for the corresponding store. However, in the case of a wireless communications devices installed by other providers, since information about a store in which these wireless communications device are installed cannot be obtained, for the corresponding store, there are difficulties in providing location-based services via indoor location measurement or suitable service contents.